In the realm of automated decision-making systems, the objective is to develop a policy that not only observes the present state of the system but also computes the optimal action or decision to be implemented. The quality of learning is deeply entrenched in the system’s exploration of the problem, a process involving a broad sweep of the state space and experimental engagement with a range of actions or decisions. The focus of this power talk will revolve around diverse exploration strategies and their impact on training quality.
For a nuanced understanding of this subject, this talk will also consider the exploration-exploitation dilemma from a generative AI perspective. It will draw parallels between generative models and exploration.
Key Takeaways:
- Understand the exploration-exploitation dilemma in automated decision-making systems
- Gain insights into how generative AI can inform exploration strategies
- Explore different exploration strategies and their effects on the quality of training
